Source code for torchio.transforms.preprocessing.label.one_hot

import torch.nn.functional as F  # noqa: N812

from .label_transform import LabelTransform


class OneHot(LabelTransform):
    r"""Reencode label maps using one-hot encoding.

    Args:
        num_classes: See :func:`~torch.nn.functional.one_hot`.
        **kwargs: See :class:`~torchio.transforms.Transform` for additional
            keyword arguments.
    """
    def __init__(self, num_classes: int = -1, **kwargs):
        super().__init__(**kwargs)
        self.num_classes = num_classes

    def apply_transform(self, subject):
        for image in self.get_images(subject):
            if image.num_channels > 1:
                message = (
                    'The number of input channels must be 1,'
                    f' but it is {image.num_channels}'
                )
                raise RuntimeError(message)
            data = image.data[0]
            num_classes = -1 if self.num_classes is None else self.num_classes
            one_hot = F.one_hot(data.long(), num_classes=num_classes)
            image.set_data(one_hot.permute(3, 0, 1, 2).type(data.type()))
        return subject